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Friday, May 24, 2019

In a previous article, Estimates of household debt in India, we presented some basic facts about household borrowings in the months of May - August 2018. We found that 46% of households in India had debt outstanding. There was a significant reliance on informal sources for the purpose of borrowing. Consumption expenditure was the most important purpose for which households borrowed followed by housing and business.

In this article, we use the same dataset, the Consumer Pyramids Household Survey (CPHS) to understand household borrowing across time. We present metrics on household indebtedness across 3 waves (January-April 2016, January-April 2017, January-April 2018). We have chosen the first wave of each year so that comparisons are made at the same point of time each year. We use household weights for each wave provided by CPHS to get population estimates for share of households having debt and the distribution of debt across different sources and purposes. CPHS does not provide information on the value of debt outstanding. Our analysis, therefore, is restricted to understanding the proportion of households borrowing from various sources for different purposes.

Overall level of Borrowing

Table 1 presents percentage of households having debt outstanding in Wave 1 in each of the three years. This has increased dramatically over the last three years. In January - April 2016, only 12% of the population had debt outstanding. This increased to 39% by January - April 2018. There has been an increase in household indebtedness in urban regions - by 2018, 40% of urban households had debt outstanding relative to 37% of rural households.

Table 1: Borrowing Share in Population

WAVE

NATIONAL

RURAL

URBAN

Jan16 - Apr16

33 million
(11.6%)

21.0 million
(11.1%)

11.4 million
(12.6%)

Jan17 - Apr17

67 million
(23%)

45.3 million
(23.1%)

20.8 million
(22.4%)

Jan18 - Apr18

114 million
(38.8%)

79.8 million
(40.0%)

34.3 million
(36.5%)

Share of sources in the population

Figure 1 presents the percentage of households in the population who have borrowed from the different sources. This throws up several interesting facts. First, the share of households who have debt outstanding from a bank has been steadily rising, consistent with rise in personal loans from banking data. The same is true for debt from relatives and friends. Second, there has been a reversal of borrowing from money-lenders. In 2016, less than 3% of households claimed to have debt outstanding from money lenders. In 2017, this rose to a little over 7.5%, and since then has fallen to 4.5% in 2018. Third, there has been a dramatic increase in borrowing from shops. In 2016, less than 1% of households had debt outstanding from shops. In 2018, this number was about 11%.

Figure 1:Share of borrowing in the population

Figure 2 presents the percentage of households who have borrowed from shops in each income decile. Borrowing from shops increased remarkably in 2018 across all income deciles. The increase was highest for the lowest income decile from 2% in 2017 to 15.5% in 2018. The corresponding increase in highest income decile was from 3% to 7.5%.

Figure 2:Borrowing from Shops across Income

Reasons for borrowing

Figure 3 presents the percentage of households who have borrowed for various purposes. Consumption expenditure remains the single largest reason behind household borrowing. Although in 2016 borrowing for consumption and housing were at the same level, housing grew slowly compared to consumption. Borrowings for business and repaying outstanding debt saw a sudden jump in overall share in 2018.

Figure 3:Reasons for borrowing in the population

Figure 4 presents the the percentage of household who have borrowed for consumption across income deciles. In 2016 the share of borrowing for consumption was almost equal across income deciles, around 2-3 %. However, by 2018, there was a huge difference between the deciles. The share of borrowing in the lowest income decile increased from 2% in 2016 to 21% in 2018. The corresponding rise in the highest income decile was from 2.5% to 10%. In 2018 21% of households in the lowest decile had borrowed for consumption expenditure as compared to 10% in the highest income decile.

Figure 4:Borrowing for Consumption across Income

Conclusion

In this article we have presented some facts about the change in household borrowings in India between 2016 and 2018. These are:

The percentage of households who have debt outstanding has increased from 12% of the population in 2016 to 39% of the population in 2018. The increase has been slightly higher in urban India relative to rural India.

There has been an increase in borrowing from banks, relative and friends, and shops. In fact, shops have seen the largest increase as the source of borrowing.

The incidence of borrowing from shops has been higher for the lower income deciles.

The biggest jump in the reasons for borrowing has been on consumption expenditure.

Borrowing for consumption expenditure increased more for lower income deciles.

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The authors are researchers with the National Institute of Public Finance and Policy.

There has been lot of grumbling in the Indian media about Electronic voting machines (EVM), esspecially over last 3 months, with opposition parties accusing the government of manipulating EVMs. This reached a crescendo recently when all the opposition parties joined hands and filed a petition in the Supreme Court seeking a directive to the Election Commission to cross verify the vote count reported by the EVM, with that of paper slips produced by the VVPAT (Voter verifiable paper audit trail), in 50% of the booths.

The Election Commission had consulted us about the sampling plan to be put in place to instill confidence about the sanctity of the election process. Here is our take on how to think about these questions.

The background

EVMs were first used in the Paravur Assembly constituency in Ernakulam district in the 1982 Assembly poll in about 50 booths. The CPI candidate, Mr. Pillai, defeated the Congress candidate, Mr. Jose, by a thin margin of 123 votes. Mr. Jose went to the High Court with the contention that the Representation of the People Act, 1951, and the Conduct of Elections Rules, 1961, did not empower the Election Commission to use EVMs. While the High Court dismissed the petition, subsequently the Supreme Court upheld the contention and ordered a repoll in these 50 booths. Mr. Jose won the election after this repoll.

Subsequently, the Representation of the People Act was amended, and S.61A was inserted, in December 1988, empowering the use of EVMs.

These EVMs were designed by public sector undertakings, BEL (a Defence Ministry PSU) and ECIL (an Atomic Energy Ministry PSU), who also manufacture the same. They are subjected to a thorough testing process.

The design and production are overseen by a technical expert committee consisting of senior professors in electronics and computer science from leading Indian Institutions.

The EVMs do not have any networking hardware. There is no ethernet port, no wifi or bluetooth capability, and thus it is not possible to alter or tamper the memory remotely.

The names of candidates on the EVM appear in an order that is determined by the same convention that had been followed since the sixties for the order on the ballot paper. First, the candidates of the national parties appear, in an alphabetical order of their names, then candidates of state parties (again in an alphabetical order of their names) and lastly the rest, again in an alphabetical order of their names. Thus, the order gets determined only after the last date of withdrawal of nomination.

The EVMs are distributed across the constituencies via randomisation. The EVMs used in India consist of two units: BU, the balloting unit and CU, the control unit. The BUs and CUs are distributed independently and on the day of polling, the two are connected. If one of them has been tampered or replaced, the handshake between the two units will fail and the pair will have to be replaced.

After the voting, the machine is locked by the presiding officer and then the EVM are physically sealed using the same techniques that were used to seal the ballot boxes of old.

In the parliamentary elections in 1999, EVMs were used in some constituencies and from 2004, all the elections to the parliament and to the state legislatures are conducted using the EVMs. It should be noted that in several instances, including in 2 out of three parliamentary elections – 2004, 2009 and 2014, the ruling party has lost the election.

The fact that there was no way to do a recount, even if a court ordered the same, was a reason for the Supreme Court to ask the EC to find a way of generating a paper trail. The Election Commission appointed a committee of experts, who came up with a design of the VVPAT machine and the Supreme Court ordered that the same be introduced as soon as possible for all elections.

The Supreme Court had not ordered the Election Commission to routinely carry out any cross verification of EVM and VVPAT count. The purpose of VVPAT was to have the possibility of a recount if a court so ordered, as a result of an election petition.

Since so many doubts had been raised about EVMs, the Election Commission decided that in every assembly constituency, one booth will be picked at random by a draw of lots and for the chosen booth, the VVPAT slips will be counted and cross checked with the count on the EVM. This done in order to increase public confidence in elections.

Our analysis of the recent debate

Over the last year, demands started coming up for verification of much larger number of EVMs. Various petitions were filed. The election commission engaged the two of us along with Mr. Onkar Prasad Ghosh to advise on an appropriate sample size, so if that many EVMs are randomly chosen and the machine count is verified with the VVPAT count and if no mismatches are found, then we can be confident that defective EVMs, if any, are in insignificant proportion.

We felt that given that the EVM's are assigned randomly to constituencies and that the order of names on the EVMs is determined at a late stage, only after the last date of withdrawal, it is not possible to manipulate or tamper the EVMs centrally.

Given that there is no networking component in EVM, tampering is possible only by getting physical access. If someone can get physical access to an EVM and tamper with the EVM, then the VVPAT slips can also be tampered and so validating EVM count by VVPAT count does not give any guarantee that EVM has not been tampered with. For this we must rely on the elaborate process that the Election Commission has about sealing the EVM in a bag, closing the same and storing in such a way that tampering can be detected. This was also the case with the paper ballot and ballot box method of earlier years. We are no worse with EVMs as compared with the old ways, in this regard.

Also, if some smart mind does figure out a way of changing the memory of EVMs remotely, he/she will not stop with doing so in one or two booths. Certainly, the effort will be to tamper with a larger number of constituencies so as to make an impact on the national level.

That is the reason that we took our objective to be to conclude with high degree of confidence that defective EVMs, if any, are less than a certain percentage of all EVMs in use nationwide. In our report we had taken this as 2%, but it can be 1% or 0.5%.

It is true that our suggested method does not give a guarantee at constituency level. But in our view, such a guarantee is not needed. Our sampling scheme can guarantee that the national picture has not been distorted by tampering or by a manufacturing defect. To those who have been insisting upon constituency level guarantees, we would ask: Why stop at the constituency? Should not every voter be assured that his or her vote has been counted correctly? The only way to do so without compromising privacy is to use cryptography. This is the subject of present research, with Prof. Bimal Roy, former director of ISI involved with one such initiative along with a team in UK. But when such a solution would become available, it can and will be attacked as opaque!

The Election Commission, in its affidavit to the Supreme Court, stated that in the last 2 years, over 1500 EVM counts have been matched with VVPAT counts and in all cases the matching has been perfect. Statistically, this alone is sufficient to conclude that EVM-VVAPT in use currently along with all the safeguards and practices in place are good.

We believe there is a lot of misinformation in the media. Various cases of EVM malfunction are being reported in the media and social media. These relate to local body elections or even college student union elections etc., which are outside the purview of the Election Commission. Other reports of EVM malfunction on the day of election are mostly about EVMs which fail the test before voting starts and are replaced.

While our recommendation was to draw a random sample from the population of all the EVMs in use, the EC decided to continue with their policy (for operational simplicity) to draw one booth per assembly segment, which translates to verifying 4125 EVMs by cross checking VVPAT counts. Based on the data about the number of booths across 4125 assembly segments, we were able to assert that if no defective is found in these 4125 chosen booths, we can say with 99.99999% confidence confidence that the proportion defective is less than 1%. This bound is true irrespective of the configuration of the defective EVMs. For example, a group of miscreants could have tried to tamper with EVMs selectively across a few constituencies. However, as long as the number of defective EVMs is 1% or more, the sampling procedure will catch this with a high probability.

The Supreme Court has ordered or suggested that instead of 1 per segment, 5 EVMs per assembly segment be drawn and VVPAT count and EVM count be cross verified. Based on our calculations, we conclude that if there are no defectives found in the 20625 randomly chosen booths (5 per assembly segment), then with 99.99999% confidence, the proportion of defectives, if any, are less than 0.25%, irrespective of the configuration of the defectives.

Saturday, May 11, 2019

Burton Malkiel's design of a newsletter scam

Start 16 newsletters. In 8 of them, scream for a year that Nifty will go up, and in 8 of them, do the opposite.

At the end of year 1, you have 8 successful newsletters. Close down the losers. Now repeat this, with 4 forecasting up and 4 forecasting down.

At the end of year 2, you have 4 successful newsletters, shut down the others.

At the end of year 3, you have 2 successful newsletters.

At the end of year 4, you are solid gold: you are holding one newsletter which correctly timed the market for 4 years in a row. Now make a lot of money selling subscriptions to this newsletter.

While this is a neat design of a scam, the world is actually, inadvertently, running something like this. A large number of newsletters are born every year. Some of them are lucky, they forecast the market correctly, and they stay alive. The losers tend to shut down.

At every point in time, you see a pool of successful newsletters. This need not imply that they have forecasting capabilities. It could just be survivorship bias at work.

Fund management

This same idea would work in fund management. You could start 16 funds, and at the end of 4 years, you would be holding 1 fund with a remarkable track record. This is possible even if you have no ability to forecast asset prices at all.

Once again, the world is actually running such a system. A large number of money managers spring up all the time. When the bets don't work out, the organisation collapses. The survivors stay in the game.

The world is initiating much more than 16 funds. Thousands of fund managers take a stab at the trade. It is not surprising that at any point in time, we see five or ten of them with five or ten years of a successful track record. While some ability may exist in the world, there is certainly a simple process of survivorship bias going on, which generates a few fund managers with a good track record.

Bellwether constituencies

Suppose you have 500 constituencies, and suppose all election outcomes are roughly 50/50. That is, there are exactly two parties and they each win about half the seats. Suppose that in truth, the outcome of each constituency is completely random and it is just like tossing a coin.

At the end of one election, you have 250 constituencies where the winner of the overall election won.

At the end of two elections, you will have 125 constituencies which were with the winner for two elections in a row.

At the end of three elections, there will be 62. At the end of four elections there will be 31.

This tells us that if we see about 30 constituencies in India, where the winner in each of these constituencies was the ruling coalition that came out of the Lok Sabha elections for 1999, 2004, 2009, and 2014, this might just be simple randomness at work. There may be nothing special about these `bellwether constituencies'.

Wednesday, May 01, 2019

Job Opening:

Manager, Centre for Development Economics, Delhi

Centre for Development Economics (CDE) is a research adjunct of the Delhi School of Economics (Department of Economics). It was established in 1992 with a grant from the Ministry of Finance, Government of India. CDE is a small non-profit organisation that supports research activities of the Department of Economics. This includes, inter alia, organising international and national seminars, workshops and lectures, managing research projects, hosting academic visitors, providing IT services to faculty and students and any other research support department faculty may require. The management of CDE is through a Council comprising faculty members.

As the administrative head the Manager has a leadership role and is responsible for managing all of the above activities. Inter alia, s/he (i) communicates with national and international funding agencies, (ii) handles logistics of prestigious international and national conferences and workshops, (iii) coordinates with CDE auditors to ensure accounts are maintained properly and with faculty members in maintaining project records. In these functions s/he is assisted by office staff that report to her/him and is guided and mentored by faculty.

Minimum Educational Qualifications:

Bachelor’s degree in any subject. Other things equal a higher degree would be an advantage.

Work experience:

At least 2-3 years in a similar role.

Other requirements:

Proven track record of strong written and oral communication skills including the ability to independently draft letters, memos and emails.

Demonstrable fluency in written and spoken English is essential.

High level of competence with commonly used software such as Word, Excel and PowerPoint.

Ability to manage research projects.

Ability to manage a team of 6-7 staff.

The ideal candidate will be a dynamic self-starting person with a willingness to learn. This position offers high visibility and scope for professional growth. An earlier occupant of this position has gone on to become Registrar at a top university.

Remuneration will be based on qualifications and experience. It includes medical insurance and leave benefits.

Please apply with a CV and names of three references to jobs@econdse.org

The position is available immediately.

Applications will be accepted till it is filled.

Only shortlisted candidates will be contacted and invited for a face to face interaction.

Monday, April 22, 2019

The borrowing of banks through deposits

When a household deals with a bank, there is a clear promise by the bank, that the deposit will be redeemable at par with some interest that is known up front. But how is a household to verify that the promise will be met at future dates? Monitoring a bank every day is hard for unsophisticated investors. Unsophisticated households face asymmetric information, a market failure.

In order to address this market failure, we do two things in financial regulation. First, we have micro-prudential regulation. The regulator coerces banks to bring down their failure probability to an acceptable level. A good thumb rule for Indian conditions is to aim for a failure probability of 2% on a one-decade horizon. This requires two elements of work: forcing banks to mark their assets to market so that bad loans are valued at fair market value, and a leverage rule which caps the leverage of banks. Second, we require a Resolution Corporation to deal with bank failure: a specialised bankruptcy process, which pays out deposit insurance to households (Rai, 2017).

This is the well understood regulatory apparatus that is brought into play when banks borrow through bank deposits, which go alongside high intensity promises.

Resource mobilisation by firms through the securities markets

How should we think about households and investment in securities (equity or debt)? Conversely, what should a financial regulatory apparatus do when a firm (a bank, an NBFC or a non-financial firm) wants to issue shares or bonds on the primary market?

A key difference on the stock market or the bond market is the lack of a promise. No promise is made, either about liquidity or about the price at which a future transaction will take place. This immediately improves the situation from a regulatory standpoint. Investors walk into buying bonds or shares with their eyes open, no promises are made to them.

Hence, we do not need to worry about micro-prudential regulation of the issuer when an investor buys shares or bonds on an exchange.

What about the primary market? In a primary issue, there is the risk of an advertising campaign that makes lurid promises to unsophisticated investors. This is addressed nicely by having a rule which requires that a minimum x% of the primary issue (of either bonds or shares) be purchased by sophisticated investors, and these investors be locked in for a certain short period. A good definition of a `sophisticated investor' for this purpose is a person who invests a minimum of Rs.10 million in the issue. Once the issue passes the market test of appealing to such investors, it is safe for households to participate directly in the primary market for securities.

Under such conditions, the gatekeepers for resource mobilisation through the primary issuance of shares or bonds are sophisticated investors and not the state. If a firm had poor prospects, or mispriced its securities, it would not get the support of these investors, and the issue would fail. How much leverage, and what debt characteristics, are appropriate for a highway or a steel company or an NBFC? There is no need for the government to get involved in terms of micro-prudential regulation or interference in the price. The only role of the state is in the adequacy and truthfulness of disclosures that are made at the time of the issue.

As there is no high intensity promise by an NBFC, failed NBFCs should go to the ordinary IBC process. The need for the Resolution Corporation, in handling firm default, is only when a systemically important NBFC fails.

When a bank borrows using the bond market, this changes the overall leverage of the bank, and the bank would of course have to comply with micro-prudential rules that cap its leverage. But there is nothing special about the primary issue of a bank, when compared with the reasoning above.

Conclusion

NBFCs in India are facing many difficulties. However, micro-prudential regulation of NBFCs is not the answer. There is no need for the state to get involved, or engage in micro-prudential regulation, of bond issues by banks, NBFCs (Roy, 2015; Shah 2018) and non-financial firms.

The sophisticated investors on the primary market are the gatekeeper; unsophisticated households free ride on their price discovery.

The Companies Act should not interfere in the bond issuance of companies, and RBI should not micro-prudentially regulate NBFCs.

The reticence of the bond market in lending to some NBFCs, from August 2018 onwards, is market discipline at work.

Thursday, April 11, 2019

The Insolvency and Bankruptcy Code (IBC) was enacted in 2016. At the time, there were a large number of zombie firms in the Indian landscape, where the first default had taken place a while ago. This motivates a question: Does the IBC perform differently when confronted with a recent default, compared with the outcomes obtained when dealing with legacy problems? In what form does the difference manifest itself?

Our prior is that old cases would fare worse, for two reasons. First, there is a selection bias. The defaulting firms who were relatively stronger, are more likely to have found a solution through successful private negotiations. The survivors are likely to be the zombie firms who would be in bad shape. Second, a firm that defaults is a melting ice cube. Every day, value is destroyed. The very fact that the default in these old cases took place a while ago suggests that substantial value destruction may have taken place. In a sound bankruptcy process, the firm is brought to the insolvency resolution process rapidly, which increases the chances of rescuing a viable business under a new ownership and balance sheet. These old cases did not have access to a sound bankruptcy process, until now.

If such a phenomenon is at work, the unconditional performance of the IBC (example, example, example) will tend to be understated. The correct measure of how well the IBC works should then be seen by restricting the analysis to recent defaults only.

In this article, we show some early small sample evidence, where IBC outcomes of old and new defaults are compared. We look at two metrics: the final outcome (liquidation or resolution), and in the class of firms that did go through a resolution, the time taken and the recovery rate. We find that a larger proportion of the old, legacy cases were liquidated. Median recovery rates were lower for the old cases that did go through a resolution compared to the more recent cases. However, there was no difference in time taken to complete the IRP process.

Institutional setting

We do not have access to the date of default. This information is not disclosed by either the National Company Law Tribunal (NCLT) or the Insolvency and Bankruptcy Board of India (IBBI). We identify old cases as those where resolution began under the Sick Industrial Companies Act, 1985 (SICA) and have remained pending at the Board for Industrial and Financial Reconstruction (BIFR).

With the repeal of SICA, the Eighth Schedule of IBC explicitly provides for the abatement of the existing SICA cases pending at BIFR and Appellate Authority for Industrial and Financial Reconstruction (AAIFR), with an option to re-initiate them as new cases under IBC.

Data

We use two sources of data. The first comes from the IGIDR Finance Research Group's (FRG) database on bankruptcy. The dataset contains all unique debtor firms from January 1, 2017 till December 31, 2018. This provides us a macro picture on the number of liquidations and resolutions of BIFR and non-BIFR firms.

The data on resolutions is sourced from the IBBI which has obtained the information from resolution professionals. In this databse we see 82 firms where resolution plans have been approved by the NCLT as of December 31, 2018. This is the most recent data available as of now. We remove the outliers from our sample, where the realisation amount exceeds the total amount of claims filed. This gives us a dataset with 72 cases. An interesting feature of this data is that the BIFR cases are smaller, with a mean liquidation value of Rs.1.75 billion, as compared with the mean liquidation value of Rs.6.25 billion for the non-BIFR firms.

Q1: Do we see more liquidations of BIFR firms?

According to the FRG database, there were 1000 ongoing cases of bankruptcy. 304 firms had been liquidated while 79 had undergone resolution. Table 1 provides a break-up of BIFR and non-BIFR firms depending on their resolution status.

Table 1: Comparison of BIFR & non-BIFR cases: Resolution status

Legacy (BIFR) cases

Fresh (non-BIFR) cases

Liquidation

220

84

Resolution

28

51

This suggests that most of the firms that had been in BIFR and that came to IBC, went into liquidation. This is not surprising as substantial value destruction would have taken place just by being in the process for many years, and it is highly unlikely that these firms had any value as a going concern.

Q2: Do we see a difference in time and recovery rate of the
approved resolutions?

Using the information from the IBBI dataset, we calculate the average recovery rates and the average time taken for the legacy (BIFR) and recent (non-BIFR) cases, as shown in Table 2. The recovery rate is estimated as the ratio of the realisable amount and admitted claims of the respective classes of creditors (FCs and OCs). This is not calculated on a net present value (NPV) basis and hence needs to be interpreted accordingly. We do not have access to information on realisable amount on an NPV basis.

Table 2: Comparison of BIFR & non-BIFR cases: Time and Recovery

Legacy (BIFR) cases

Fresh (non-BIFR) cases

Number

24

48

Mean time to resolve (days)

311

311

Median time to resolve (days)

282

282

Mean recovery rate (%)

46.23

47.63

Median recovery rate (%)

32.69

42.77

Mean recovery rate of FCs (%)

45.57

49.01

Median recovery rate of FCs (%)

35.70

42.75

Mean recovery rate of OCs (%)

36.04

35.44

Median recovery rate of OCs (%)

11.92

19.84

The main feature of these results is that the sample means and the medians of the two groups are rather alike on a number of parameters. The average and median time taken to resolution is the same for the two groups. The mean recovery rate is around 47% for both groups. There is, however, almost a 10 percentage point difference in the median recovery rate - once again not surprising given that BIFR firms are likely to have seen significant value destruction before they came to the IBC. The same pattern is repeated when one looks at recovery rates of financial and operational creditors between the two groups of firms.

Conclusion

Our prior was that the legacy cases have been festering for long and hence have experienced prolonged value depreciation, and would therefore see different outcomes than fresh defaults. We find the difference in the form of more liquidations of the legacy firms, and significant differences in liquidation value, and median recovery rates. We, however, do not find any difference in the time taken to complete the insolvency resolution process. This could be because the legacy cases which were in worse shape went into liquidation and those that remained in IRP were not very different in nature compared to the recent defaults.

This also motivates thoughts for further research. Will this result hold up with stronger datasets that maybe visible in a year or two? This kind of analysis, conducted on a bigger dataset spanning a few years post IBC, is likely to give a more realistic picture of the performance of IBC as opposed to analyses which view the legacy and recent cases from the same perspective.

Tuesday, April 09, 2019

In late 2017, the government introduced the Financial Resolution and Deposit Insurance (FRDI) Bill which proposed a new resolution framework for banks and financial firms. It planned an overhaul of the present system operated jointly by the Reserve Bank of India (RBI) and the Deposit Insurance and Credit Guarantee Corporation (DICGC), and introduce a modern Resolution Corporation with more extensive powers to regulate and resolve banks.

The bill faced resistance in Parliament. The opposition stated that the bill risked the solvency of public sector banks and accused the government of putting public money at risk. Some argued that the current system of deposit insurance would be taken away by the proposed FRDI law. The Bill was said to have been “designed to punish small depositors for the sins of defaulters, corrupt bank managers and political masterminds”. The bill was withdrawn in 2018. As a result, the DICGC has remained the insurer for bank deposits.

In this article, we attempt to measure how the DICGC has fared in processing bank failures and settling claims of depositors.

Present system

Today, the DICGC acts as a pay-box. Under its eponymous law of 1961, the DICGC insures the deposits of banks created by parliamentary (central) legislation, private banks, and eligible co-operative banks. Eligible co-operative banks are a subset of co-operative banks where the state legislature has empowered the RBI to exercise some regulatory oversight. Under the 1961 Act, if a bank insured by DICGC is wound up or has its license cancelled by the RBI, every depositor is entitled to insurance of up to Rupees 100,000.

The law envisages a quick payout to reduce inconvenience to depositors due to a bank failure. Within three months of being appointed, every liquidator of a bank must provide to DICGC, a list of depositors with the amount due to each one of them (S.17(1)). The DICGC, then makes the insurance payout. The law (S. 17 (2)) requires DICGC to pay claims within two months of receiving the list from the liquidator.

Data

We source data from annual reports of the DICGC and RBI notifications concerning the cancellation of license/ de-registration of 127 banks from 2013 to 2018. From these, we note the year of payout under the DICGC Act, number of depositors, and the amount of claims settled. We were able to trace RBI notifications for 115 banks, which accounted for 95% of all depositors, and 99% of the amount of payouts. We measure the time taken between the RBI notification (which we assume to be analogous with the exit of the bank), the disbursement of the payments; and the opportunity cost of the amount of claims at 8% and 20% per annum.

Overall performance

Bank failures are especially disruptive for depositors. Banks work on the promise of providing deposits callable at par. A bank makes a promise to allow the depositor to withdraw their money within one banking day. This gives confidence to the depositor to use banks for their daily needs rather than hoarding cash. Though there have been no commercial bank failures in India in the recent past, this hides a deep and persistent problem of co-operative bank failures in India.

Cases of Bank Failures handled by the DICGC from 2013 to 2018

Year

Number of Payouts

Depositors

Claims (Rupees million)

2013-14

51

96590

1030.93

2014-15

30

185901

3212.89

2015-16

17

90792

471.44

2016-17

10

35215

586.37

2017-18

19

56173

435.19

Total

127

464671

5736.82

As the table above shows, over the last five years, 25 co-operative banks have received payouts by DICGC each year on an average. More than 400,000 depositors had to use the deposit insurance scheme. These are not small numbers. Their experience of the deposit insurance payout will be the basis of the trust they will repose in the banking system, the regulator, and the deposit insurance scheme.

Time taken to disburse payments

Under the DICGC Act, the liquidator is supposed to provide a list of claimants to DICGC within three months of her appointment. However, a major cause of delay for the payouts is that the claims list is not received from the liquidator within the stipulated time limit. This may be because cases filed against the liquidator are in court, appeals by the bank are pending before the Ministry of Finance (Appellate Authority), or clarifications are required about the claims list.

The process of disbursement of payments needs to be expedient so that depositors do not suffer undue losses and maximum value is derived from the failed institution. To measure the effectiveness of DICGC we calculate the opportunity cost of the delay in payments.

Money today is worth more than money tomorrow. Since the depositors are unable to access their money, which was promised to be to be callable at par, they face a loss. Depositors have three choices (i) find an alternate source/borrow, (ii) forgo consumption or, (iii) forgo investment. The time taken by DICGC for the disbursement of the due amount imposes a cost on the depositors. The standard measure for delayed payment is the opportunity cost of the money. It is calculated by discounting the amount with a discount rate over the time by which it was delayed. For example, if the depositor is owed Rs. 100; and the insurance payout delay is 1 year; and the discount rate is 10%, then a payout of Rs. 100 at the end of the year is effectively a payout of only Rs. 91. If the payout is delayed by 2 years; effective payout is Rs. 82.64.

We use two rates of discounting to measure the opportunity cost: 8% and 20%. 8% is slightly above the risk-free rate of return and can be considered as the minimum opportunity cost of money. However, if you are poor or an individual, it is almost impossible to borrow at this rate. So we choose another realistic rate of 20%. This is the rate at which the government requires buyers to compensate MSMEs for delayed payments (S. 16, MSMED Act).

As the graph above shows, it took an average of 2.10 years for the disbursement of the amount by DICGC. This is five times the statutory limit of five months. Similarly, the median time taken is more than four times the statutory limit. The pay-out process took over 5 years in almost 1/3rd of the cases of failures. A reading of the Annual Reports of DICGC shows that in two cases of bank failure, it took over 14 years for the insurance claims to be disbursed.

As of 20th March 2019, there were another 25 bank failures pending before DICGC, where depositors have been waiting for an average of 6.57 years. Two of these banks were de-registered 20 years ago and DICGC is yet to receive the claims list from the liquidator.

Year-wise metrics of delay in disbursement

Year

Average Time Taken (years)

Claims (million)

Opportunity Cost at 8% (million)

Opportunity Cost at 20% (million)

2013–14

1.16

1030.93

930.18

814.21

2014–15

2.31

3212.89

2642.44

2030.18

2015–16

1.35

471.44

410.30

342.16

2016–17

2.16

586.37

496.86

399.19

2017–18

3.86

435.20

322.56

216.32

Total

2.10

5736.83

4802.34

3802.05

As Table 2 shows, opportunity costs have risen over the years. At a conservative discount rate of 8%, depositors in 2017-18 effectively got only Rs. 322.56 million, for claims of Rs. 435.2 million, a 26% loss. This is due to the increasing delays in processing payouts. In 2013-14, the loss rate was only 10% of the value of insurance. However, 8% is an optimistic rate. At a realistic rate of 20%, depositors lost more than 50% of the value of payouts in 2017-18. At the realistic rate, depositors have lost over one third the value of their insurance in the past five years due to delays.

Proposed System

One of the reasons for the delay is due to the fact that that DICGC itself does not have the power to obtain the list of depositors in a bank. It has to depend on the liquidator to provide the list. This explains a significant portion of the delays. As shown here, in a number of cases the DICGC is waiting for the liquidator to provide the list of insurance claimants.

The FRDI Bill solves this problem through two measures:

Allowing the Resolution Corporation to take over the management of a bank at risk before it stops banking activities and is bankrupt.

Combining the function of the liquidator/receiver and insurer in the same agency.

The Bill proposed a mechanism for an early warning system for banks at risk of failure. Banks would be classified into five categories ranging from low to critical. If a bank was classified in higher risk categories it would have to formulate its resolution plan which would include information about depositors [S.44]. The bill also allowed the Resolution Corporation to take over the management of banks at critical risk (S. 46) which is before bankruptcy (unlike the present system). This would give the Resolution Corporation the power to get into the books of the bank before it failed and consequently give it more time to make a list of insurance claimants without delay to depositors.

This is unlike the present system where the DICGC enters the process only after the bank has been declared insolvent by the RBI and is unable to pay its dues (see here, here and here). By coming in before a bank has failed, the Resolution Corporation would have the ability to analyse its operational statements, books of records, and list of depositors entitled to payouts in the event of such a failure.

Another cause of the delay is that the liquidator and DICGC are independent of each other. The liquidator is appointed after a Bank has been ordered to be wound up, and is not part of the DICGC. The DICGC hence has no authority in preparing the list of eligible claimants and has to depend on the liquidator to provide it. In contrast, the proposed Resolution Corporation would have acted as the liquidator and insurer for a co-operative bank in the event of a failure (S. 62). As such, the corporation would not have to depend on an external party to prepare a list of claimants.

Conclusion

The process under the current regime is slow. It takes close to two years after the cancellation of license for the disbursement of claims. Since most co-operative banks service poorer clients, their failure hurts people who are not in a position to forgo their deposits for long periods of time. Today, without a framework for bankruptcy and orderly resolution for financial firms, India faces the risk that if a large private sector bank goes bankrupt, the depositors could be stuck for years before getting their money back.

The focus of the deposit insurance corporation needs to shift from payouts after default, into the problem of identifying weak banks and stopping them. Many countries have developed specialised resolution regimes for various categories of financial firms. The Financial Stability Board recommends operational independence as a key attribute of resolution regimes. In several jurisdictions, including the USA, Canada, Malaysia, Mexico, Japan, Korea, etc., these are in the form of separate institutions with resolution powers.